The efficacy of traditional Chinese medicine (TCM) treatments for Western medicine (WM) diseases relies heavily on the proper classification of patients into TCM syndrome types. The authors developed a data-driven method for solving the classification problem, where syndrome types were identified and quantified based on statistical patterns detected in unlabeled symptom survey data. The new method is a generalization of latent class analysis (LCA), which has been widely applied in WM research to solve a similar problem, i.e., to identify subtypes of a patient population in the absence of a gold standard. A well-known weakness of LCA is that it makes an unrealistically strong independence assumption. The authors relaxed the assumption by first detecting symptom co-occurrence patterns from survey data and used those statistical patterns instead of the symptoms as features for LCA. This new method consists of six steps: data collection, symptom co-occurrence pattern discovery, statistical pattern interpretation, syndrome identification, syndrome type identification and syndrome type classification. A software package called Lantern has been developed to support the application of the method. The method was illustrated using a data set on vascular mild cognitive impairment. Copyright © 2017 Journal of Integrative Medicine Editorial Office.
CitationZhang, N. L., Fu, C., Liu, T. F., Chen, B.-X., Poon, K. M., Chen, P. X., et al. (2017). A data-driven method for syndrome type identification and classification in traditional Chinese medicine. Journal of Integrative Medicine, 15(2), 110-123.
- Medicine, Chinese traditional
- Syndrome classification
- Latent tree analysis
- Symptom co-occurrence patterns
- Patient clustering
- Stand syndrome differentiation